Porównaj metody

	Adaptacja domenowa	Klasyfikacja Tekstu
Dziedzina	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	—	—
Twórca	—	—
Typ	NLP transfer-learning / fine-tuning pipeline	Supervised NLP classification task
Źródło pierwotne	Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Inne nazwy	Alan Uyarlaması (Domain Adaptation) — NLP, domain adaptation NLP, domain fine-tuning	text categorization, document classification, topic classification, metin sınıflandırma
Pokrewne	4	4
Podsumowanie	Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.